<span id="vlvnn"><dl id="vlvnn"></dl></span> <span id="vlvnn"><dl id="vlvnn"></dl></span><strike id="vlvnn"><dl id="vlvnn"><ruby id="vlvnn"></ruby></dl></strike>
<span id="vlvnn"><video id="vlvnn"><strike id="vlvnn"></strike></video></span>
<ruby id="vlvnn"><i id="vlvnn"></i></ruby>
<span id="vlvnn"></span><ruby id="vlvnn"><i id="vlvnn"></i></ruby>
<span id="vlvnn"></span>
<span id="vlvnn"><video id="vlvnn"></video></span>
<del id="vlvnn"><progress id="vlvnn"></progress></del>
<strike id="vlvnn"><i id="vlvnn"><del id="vlvnn"></del></i></strike><span id="vlvnn"></span>
<strike id="vlvnn"></strike>
<span id="vlvnn"></span>


The approach to address the client’s challenge included:

  • Setting up a central Cloud-Data warehouse as a single source of truth
  • Laying the foundational data architecture that classified requests into zones based on repeatability and use
  • Implementation of a metadata-driven governance system focused on UI to capture datarequests
  • Building re-usable frameworks to enable the platform to serve personas with data requests, platform usage requests and insights requests
  • Harnessing the power of Data-Bricks on Azure to create a dynamic, auto-scalable ingestion layer based on workload


  • Azure native solution easily integrable with Collibra governance platform powered by Rest-API for an organization wide transparency
  • Manages record changes via unified streaming, and in-built data-quality layer using the delta-lake feature of data-bricks
  • Loosely coupled frameworks for logging/Alerting & monitoring which are cloud-agnostic and can be re-configured and used as needed


  • The time taken to on-board data decreased to a 24-hour interval
  • Overall storage and computing cost reduced by 30%
  • One platform – to bring your own data, Platform & data services and Insights services